class_docstr: A pretrained model implementation for Grapheme-to-Phoneme (G2P) models
  that take raw natural language text as an input and
  
  Example
  -------
  >>> text = ("English is tough. It can be understood "
  ...         "through thorough thought though")
  >>> from speechbrain.inference.text import GraphemeToPhoneme
  >>> tmpdir = getfixture('tmpdir')
  >>> g2p = GraphemeToPhoneme.from_hparams('path/to/model', savedir=tmpdir) # doctest: +SKIP
  >>> phonemes = g2p.g2p(text) # doctest: +SKIP
code: def __init__(self, *args, **kwargs):
      super().__init__(*args, **kwargs)
      self.create_pipelines()
      self.load_dependencies()
  
imports: import torch
  from itertools import chain
  from speechbrain.inference.interfaces import Pretrained, EncodeDecodePipelineMixin
label_desc: Use image node_id 1 to create a new GraphemeToPhoneme object from inherited base classes: Pretrained, EncodeDecodePipelineMixin with example: obj = GraphemeToPhoneme()